Error propagation dynamics of PIV-based pressure field calculations: How well does the pressure Poisson solver perform inherently?

Pan, Zhao; Whitehead, Jared P.; Thomson, Scott L.; Truscott, Tadd

doi:10.1088/0957-0233/27/8/084012

Cited by 49 publications

(50 citation statements)

References 37 publications

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“…The implementation of boundary conditions can have considerable effects on the accuracy of pressure estimates (Pan et al 2016). For the iterative methods (omni-directional, eight-path, and local least squares), the boundary conditions were implemented following the approach employed in the studies that proposed these techniques (Liu and Katz 2006;Dabiri et al 2014;Tronchin et al 2015), namely, where the domain was initialized to zero pressure before integrating the pressure gradient over the inner domain and boundaries.…”

Section: Synthetic Piv and Pressure Estimation Optimizationmentioning

confidence: 99%

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

McClure

Yarusevych

2017

Exp Fluids

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Section: Synthetic Piv and Pressure Estimation Optimizationmentioning

confidence: 99%

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

McClure

Yarusevych

2017

Exp Fluids

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“…The omni-directional integration method introduced by Liu and Katz ( 2006) is capable of efficiently minimizing the influence of local errors in the material acceleration on the final reconstructed pressure field. The Poisson-based pressure integration is instead known to be more prone to error propagation, especially when pure Neumann conditions are used in large domain (Pan et al, 2016). Nevertheless, the extension of the original 2D path integration method to 3D domains makes the number of required integration paths so large that a computational parallelization is necessary to make this pressure integration strategy affordable in terms of computational time (Wang et al, 2019).…”

Section: Pressure Reconstructionmentioning

confidence: 99%

3D pressure field reconstruction from time-resolved stereoscopic PIV measurements by relaxation of Taylor's hypothesis

Fratantonio¹,

Charonko²

2021

ISPIV21

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This work presents reconstructions of 3D pressure fields starting from 2D3C stereoscopic-PIV (SPIV) measurements. In Fratantonio et al. (2021), we presented a new reconstruction algorithm, the “Instantaneous convection” method, capable of producing 3D velocity fields from time-resolved SPIV measurements. For reconstructions in flows with strong shear layers and high turbulence intensity, this method is able to provide time-resolved 3D velocity volumes that are more accurate than those that can be obtained from the more frequently employed reconstruction method based on the Taylor’s hypothesis and on the use of a mean convective field. Here we investigate the possibility of reconstructing the 3D pressure field from the timeresolved series of reconstructed 3D velocity data. A pseudo-tracking method is employed for computing the velocity material derivative, and the pressure field is then reconstructed by solving the 3D Poisson equation. The velocity and pressure reconstructions are validated on the Direct Numerical Simulation data of the turbulent channel flow taken from the John Hopkins Turbulence Database (JHTDB), and an application to experimental SPIV measurements of an air jet flow in coflow carried out at the Turbulent Mixing Tunnel (TMT) facility at Los Alamos National Laboratory is presented.

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“…6 is used to make the velocity satisfy the divergence-free condition. As analyzed in the PIV-based pressure measurements, the error will propagate from the velocity field to the pressure field (de Kat and van Oudheusden 2012;Pan et al 2016). de Kat and van Oudheusden (2012) proposed that the RMS error of the pressure p for Eulerian form can be given as figure (a, b), the PIV-PCS with BCs-Raw and BCs-POD are compared with median filtering , DCT-PLS and DFS .…”

Section: The Performance In Terms Of Estimating Pressurementioning

confidence: 99%

Error reduction for time-resolved PIV data based on Navier–Stokes equations

et al. 2018

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The post-processing of the measured velocity in particle image velocimetry (PIV) is a critical step in reducing error and predicting missing information of the flow field. In this work, time-resolved PIV data are incorporated with the incompressible Navier-Stokes (N-S) equations to reduce the measurement error and improve the accuracy. A pressure correction scheme (PCS) based on the projection method is adopted to solve the N-S equations, and an optimization algorithm is introduced to balance the fidelity between the PIV data and the numerical solutions. The PCS for PIV data, called PIV-PCS, cannot only reduce the errors in the velocity divergence and the curl of the pressure gradient but also ensure that the flow field satisfies the dynamic constraints imposed by the momentum equation. An important weight coefficient s that balances the level of the velocity modification with the residual of the governing equation is defined and numerically assessed. A method for optimizing the value of s is provided. The new approach is evaluated by two time-resolved PIV experiments: one on the 2D wake flow of a circular cylinder at low Reynolds number and one on tomographic PIV for the 3D wake flow of a hemisphere at high Reynolds number. All the numerical assessments and experimental applications are compared with the divergencefree smoothing (DFS) method. The results indicate that the presented PIV-PCS method is superior to the DFS method in terms of reducing the measurement error and recovering the real physical flow structures.

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Error propagation dynamics of PIV-based pressure field calculations: How well does the pressure Poisson solver perform inherently?

Cited by 49 publications

References 37 publications

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

3D pressure field reconstruction from time-resolved stereoscopic PIV measurements by relaxation of Taylor's hypothesis

Error reduction for time-resolved PIV data based on Navier–Stokes equations

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